spring-ai-examples/agents/reflection

Spring AI Hello World Chat Application

A simple command-line chat application demonstrating Spring AI's ChatClient capabilities with AI models.

It is based on the code in the repository https://github.com/neural-maze/agentic_patterns

Prerequisites

• Java 17 or higher
• Maven

This examples uses OpenAI as the model provider.

Before using the AI commands, make sure you have a developer token from OpenAI.

Create an account at OpenAI Signup and generate the token at API Keys.

The Spring AI project defines a configuration property named spring.ai.openai.api-key that you should set to the value of the API key obtained from OpenAI.

Exporting an environment variable is one way to set that configuration property:

export SPRING_AI_OPENAI_API_KEY=<INSERT KEY HERE>

Running the Application

1. Clone the repository
2. Navigate to the project directory
3. Run the application using Maven wrapper: ./mvnw spring-boot:run

Reflection Agent Demo

This project demonstrates the use of Spring AI to create a self-improving code generation system. The Reflection Agent uses two ChatClient instances in an iterative loop - one for generation and one for critique - to produce high-quality Java code.

Overview

The application implements a reflection-based system where the Reflection Agent:

1. Uses a generation ChatClient instance to create code based on user prompts
2. Uses a critique ChatClient instance to review the generated code
3. Iteratively improves the code by feeding critique back to the generation ChatClient
4. Continues this loop until the critique ChatClient is satisfied with the quality

Project Structure

Main Components

• Application.java: The main Spring Boot application that provides the command-line interface
• ReflectionAgent.java: The core component that manages the iteration between generation and critique

How It Works

Initial Setup

The Reflection Agent creates two ChatClient instances:

• generateChatClient: For generating Java code based on user requests
• critiqueChatClient: For reviewing and critiquing the generated code

Generation Process

• User inputs a request
• The generation ChatClient creates initial code
• The critique ChatClient reviews the code
• If improvements are needed, the generation ChatClient creates a revised version
• This continues for up to maxIterations or until the critique ChatClient approves (<OK>)

ChatClient Configurations

• Generation ChatClient:

You are a Java programmer tasked with generating high quality Java code. Your task is to generate the best content possible for the user's request.

• Critique ChatClient:

You are tasked with generating critique and recommendations for the user's generated content. If the user content has something wrong or something to be improved, output a list of recommendations and critiques."

Example Run

In this sample run, the user requested a JUnit 5 test for a Person class. See the file JacksonTestAgent.md for the actual output.

Initial Generation

• The generation ChatClient created a basic Person class and test class
• Included serialization/deserialization functionality
• Implemented basic test cases

Critique Phase

The critique `ChatClient identified several improvements:

• Better error handling
• Improved code readability
• Need for edge case testing
• Better test structure
• Expanded test coverage
• Enhanced Java class structure
• Modern Java feature usage

Final Result

The generation ChatClient created improved code with:

• Separated test methods for better modularity
• Enhanced error handling with detailed messages
• Added edge case testing for null values
• Improved code structure and readability
• Better test coverage

MergeSort

The file AgentMergeSort.md shows a similar run to create a merge sort algorithm.